Short messaging has become very popular over the past few years. Many of these messages contain information or opinions that can be very valuable to companies. As a result, methods are being developed to try and identify various characteristics of these short messages.
One particular characteristic of interest is a sentiment of a short message. Current methods for detecting sentiments of text are not sufficient to detect sentiments of very short messages. Most of the current approaches use the raw word representation (or n-grams) as features to build a model for sentiment detection and perform this task over large pieces of text. In addition, these approaches may be heavily biased towards particular words when looking at the raw words of the text and provide noisy data that is not accurate. However, these techniques are not able to perform accurate sentiment detection on short messages because there is not enough information in these short messages to rely on.